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Presented by Erlend Hamberg.

An important problem in genetics is phylogenetic inference: Coming up with good hypotheses for the evolutionary relationship between species – usually represented as a “family tree”. As the amount of molecular data (e.g. DNA sequences) quickly grows, efficient algorithms become increasingly important to analyze this data. A maximum-likelihood approach with models for nucleotide evolution allows us to use all the sequence data, but is a computationally expensive approach. The number of possible trees also grows rapidly as we include more species. It is therefore necessary to use heuristic search methods to find good hypotheses for the “true” tree. Evolutionary algorithms (EA) is a class of such search/optimization algorithms that has been shown to perform well in other areas where the search space is large and irregular. I will explain my approach and my findings from using an evolutionary algorithm for inferring phylogenies from molecular data.